Such genomic aberrations were identified already more than a decade ago using array-based comparative hybridization. They can also be detected using

Such genomic aberrations were identified already more than a decade ago using array-based comparative hybridization. They can also be detected using

data from SNP genotyping arrays, typically by combining the intensities of the two probes for a given SNP and comparing to the same SNP from other arrays (thus deriving a copy number ratio).

data from SNP genotyping arrays, typically by combining the intensities of the two probes for a given SNP and comparing to the same SNP from other arrays (thus deriving a copy number ratio).

−

Significant shift from the baseline (unit ratio or zero log ratio) reflects copy number changes. Such changes can be identified in many ways, for example, one can use segmentation algorithms to partition the signal then try to classify such segments into gain, copy neutral and loss status.

+

Significant shift from the baseline (unit ratio or zero log ratio) reflects copy number changes. Such changes can be identified in many ways, for example, one can use segmentation algorithms to partition the signal then classify such segments into gain, copy neutral and loss status.

Yet, for large datasets, one can take advantage of the signal distribution at each SNP, and cluster each individual from the distribution into a component that would reflect a given copy number change.

Yet, for large datasets, one can take advantage of the signal distribution at each SNP, and cluster each individual from the distribution into a component that would reflect a given copy number change.

−

We developped a Gaussian Mixture Model, which detect copy number variation from the distribution of copy number ratios. From the data, it will fit one component for each of the following copy number states: deletion, copy-neutral, 1 and 2 additional copy; with a constraint on the difference between the mixture means. Then for a given individual, it will determine the probabilities for each copy number state and compute the expected copy number (dosage).

+

We developed a Gaussian Mixture Model, which detect copy number variation from the distribution of copy number ratios. From the data, it will fit one component for each of the following copy number states: deletion, copy-neutral, 1 and 2 additional copy; with a constraint on the difference between the mixture means. Then for a given individual, it will determine the probabilities for each copy number state and compute the expected copy number (dosage).

=== License ===

=== License ===

Line 81:

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'''* What happen if the model fails to fit the data ?'''

'''* What happen if the model fails to fit the data ?'''

−

You will model will move to the next SNP to process and you will simply get the warning :

+

The model will output this warning :

Exiting: Maximum number of iterations has been exceeded - increase MaxIter option.

Exiting: Maximum number of iterations has been exceeded - increase MaxIter option.

−

Missing data will be set as 0.

+

Missing data will be set as 0. Then the model will analyse the next SNP (if any).

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Yes, by default a Loess smoothing is applied. (This step can be skipped by setting DO_LOESS_SMOOTH=0 in the shell script or setting DO_LOESS=0; in callCNVs.m).

Yes, by default a Loess smoothing is applied. (This step can be skipped by setting DO_LOESS_SMOOTH=0 in the shell script or setting DO_LOESS=0; in callCNVs.m).

−

It is also recommended that adequate normalization is applied and that such normalized ratios are provided in the matrix input file.

+

Since Gaussian Mixture Model can be sensitive to batch effects, it is strongly recommended that adequate normalization is applied before using the model.

+

note : The loess smoothing will not correct batch effects, but will improve the signal to noise ratio within individual profile. By default, the Loess windows size is 41 SNPs. For higher density arrays (Affymetrix 6.0 or Illumina 1M) such window could be increased.

'''* I am getting this error :

'''* I am getting this error :

−

error while loading shared libraries: libmwmclmcrrt.so: cannot open shared object file: No such file or directory

+

error while loading shared libraries: libmwmclmcrrt.so:

+

cannot open shared object file: No such file or directory

what does it mean?'''

what does it mean?'''

Most likely your LD_LIBRARY_PATH is not pointing correctly to the MCR.

Most likely your LD_LIBRARY_PATH is not pointing correctly to the MCR.

Revision as of 12:58, 25 December 2009

Deletion, insertion and duplication events giving rise to copy number variations (CNVs) have been found genome-wide in the humans and other species.
Such genomic aberrations were identified already more than a decade ago using array-based comparative hybridization. They can also be detected using
data from SNP genotyping arrays, typically by combining the intensities of the two probes for a given SNP and comparing to the same SNP from other arrays (thus deriving a copy number ratio).
Significant shift from the baseline (unit ratio or zero log ratio) reflects copy number changes. Such changes can be identified in many ways, for example, one can use segmentation algorithms to partition the signal then classify such segments into gain, copy neutral and loss status.
Yet, for large datasets, one can take advantage of the signal distribution at each SNP, and cluster each individual from the distribution into a component that would reflect a given copy number change.

We developed a Gaussian Mixture Model, which detect copy number variation from the distribution of copy number ratios. From the data, it will fit one component for each of the following copy number states: deletion, copy-neutral, 1 and 2 additional copy; with a constraint on the difference between the mixture means. Then for a given individual, it will determine the probabilities for each copy number state and compute the expected copy number (dosage).

Frequently Ask Questions

The current implementation models deletion, copy neutral, 3 copies and more than 3 copies.

* What happen if the model fails to fit the data ?

The model will output this warning :

Exiting: Maximum number of iterations has been exceeded - increase MaxIter option.

Missing data will be set as 0. Then the model will analyse the next SNP (if any).

* I am getting :

Exiting: Maximum number of iterations has been exceeded - increase MaxIter option.

What does this mean?
The model could not find the component separation before reaching its maximal iteration limit.
This can be due to noisy data, or distribution where no such separation exists.
Try increasing :
MAX_FUN_CALL=10000; # nb of optimization function call
MAX_FUN_ITER=5000; # nb of iterations for each optimization function call
But note, this can significantly increase the runtime.

* Can I apply some extra normalization before fitting the Gaussian Mixture Model?

Yes, by default a Loess smoothing is applied. (This step can be skipped by setting DO_LOESS_SMOOTH=0 in the shell script or setting DO_LOESS=0; in callCNVs.m).

Since Gaussian Mixture Model can be sensitive to batch effects, it is strongly recommended that adequate normalization is applied before using the model.
note : The loess smoothing will not correct batch effects, but will improve the signal to noise ratio within individual profile. By default, the Loess windows size is 41 SNPs. For higher density arrays (Affymetrix 6.0 or Illumina 1M) such window could be increased.

* I am getting this error :

error while loading shared libraries: libmwmclmcrrt.so:
cannot open shared object file: No such file or directory

what does it mean?

Most likely your LD_LIBRARY_PATH is not pointing correctly to the MCR.
The run_callCNVs.sh script should do it for you.